Shopify Inc.

Kyle Bruce Tate

Peng Yu

Kshetrajna Raghavan

Xiaoxiao Li

Niklas Itänen

Ronie Miguel Uliana

Niklas Itaenen

Saad Atieque

Siphumelele Langeni

An e-commerce platform may be subject to fraudulent activity. In some embodiments, a graph is used to represent the connection between users of the e-commerce platform. The graph may be built and maintained using a belief propagation algorithm. The graph may be used to assign, to each user, the probability that the user is fraudulent. In some embodiments, the use of the graph and updating the graph are two independent operations: one performed in real-time, and the other performed offline. In some embodiments, additional information may be requested from a user if the user is assigned a high probability of being fraudulent, in order to try to better determine whether or not the user is a fraudulent user. The embodiments are not limited to an e-commerce platform, but may generally apply to any computer system in which there is a desire to detect fraudulent activity.

Computer-implemented systems and methods for detecting fraudulent activity

Computer-implemented methods and systems for colour-based image tagging and colour-based searching. The method may include identifying, using image analysis, one or more dominant colours of a product based on an image of the product and receiving selection of at least one of the one or more dominant colours. In response to receiving the selection of the at least one of the one or more dominant colours, a search for products matching the at least one of the one or more dominant colours may be initiated to obtain one or more results of the searching, the one or more results including at least one product matching the at least one of the one or more dominant colours.

Methods and systems for colour-based image analysis and search

A data partitioning system receives an input dataset for e-commerce products, each sample containing attributes and associated values for each product including at least an image; represents each sample as a node on a graph to provide a graph of nodes for the dataset; measures a relative similarity distance between each pair of nodes based on comparing at least image values for the attributes; determines for each pair of nodes whether they are related if the similarity distance between them is below a defined threshold, and if related, generate an edge between them on the graph; group the connected nodes into a first or a second group such that the grouped nodes have no edges connecting them to nodes in the other group and have a shortest relative similarity distance with each other. The groups are used as training dataset and testing data sets for a supervised machine learning classifier.

Systems and methods for automated product classification

Systems and methods are provided for automatically making a decision as to whether or not to over an installment payment (IP) option to a customer at checkout at an online retailer for goods or services. The IP decision is made, at least in part, based on the specific product or products being purchased. In some cases, it is also based on merchant-specific information and/or customer specific information.

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Ottawa, Canada

Kyle Bruce Tate